Overview

Dataset statistics

Number of variables12
Number of observations217
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.0 KiB
Average record size in memory104.0 B

Variable types

Text2
Numeric10

Alerts

All Grades is highly overall correlated with Total Students and 2 other fieldsHigh correlation
Total Students is highly overall correlated with All Grades and 4 other fieldsHigh correlation
Hispanic-Latino-% is highly overall correlated with All GradesHigh correlation
High is highly overall correlated with All Grades and 1 other fieldsHigh correlation
Tests Taken is highly overall correlated with Total Students and 2 other fieldsHigh correlation
% Score 1-2 is highly overall correlated with Total Students and 2 other fieldsHigh correlation
% Score 3-5 is highly overall correlated with Total Students and 2 other fieldsHigh correlation
District Name_x has unique valuesUnique
District Code has unique valuesUnique
District Name_y has unique valuesUnique
All Grades has 22 (10.1%) zerosZeros
Hispanic-Latino-% has 22 (10.1%) zerosZeros
Primary has 210 (96.8%) zerosZeros
Secondary has 174 (80.2%) zerosZeros
High has 26 (12.0%) zerosZeros
% Score 1-2 has 182 (83.9%) zerosZeros
% Score 3-5 has 184 (84.8%) zerosZeros

Reproduction

Analysis started2023-07-02 22:20:30.104514
Analysis finished2023-07-02 22:20:36.653354
Duration6.55 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

District Name_x
Text

UNIQUE 

Distinct217
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:36.938473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length61
Median length57
Mean length15.847926
Min length4

Characters and Unicode

Total characters3439
Distinct characters52
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique217 ?
Unique (%)100.0%

Sample

1st rowAbington
2nd rowAcademy Of the Pacific Rim Charter Public (District)
3rd rowActon-Boxborough
4th rowAdvanced Math and Science Academy Charter (District)
5th rowAmesbury
ValueCountFrequency (%)
district 27
 
6.5%
charter 22
 
5.3%
regional 18
 
4.3%
vocational 15
 
3.6%
technical 15
 
3.6%
school 13
 
3.1%
academy 10
 
2.4%
public 6
 
1.4%
science 5
 
1.2%
north 5
 
1.2%
Other values (240) 282
67.5%
2023-07-02T18:20:37.170489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 285
 
8.3%
o 274
 
8.0%
t 245
 
7.1%
a 236
 
6.9%
r 225
 
6.5%
i 211
 
6.1%
n 203
 
5.9%
201
 
5.8%
l 189
 
5.5%
c 148
 
4.3%
Other values (42) 1222
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2734
79.5%
Uppercase Letter 439
 
12.8%
Space Separator 201
 
5.8%
Open Punctuation 23
 
0.7%
Close Punctuation 23
 
0.7%
Dash Punctuation 18
 
0.5%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 285
10.4%
o 274
10.0%
t 245
9.0%
a 236
 
8.6%
r 225
 
8.2%
i 211
 
7.7%
n 203
 
7.4%
l 189
 
6.9%
c 148
 
5.4%
h 125
 
4.6%
Other values (14) 593
21.7%
Uppercase Letter
ValueCountFrequency (%)
S 49
11.2%
C 46
 
10.5%
D 38
 
8.7%
M 34
 
7.7%
R 29
 
6.6%
A 26
 
5.9%
W 25
 
5.7%
B 24
 
5.5%
T 24
 
5.5%
P 22
 
5.0%
Other values (13) 122
27.8%
Space Separator
ValueCountFrequency (%)
201
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3173
92.3%
Common 266
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 285
 
9.0%
o 274
 
8.6%
t 245
 
7.7%
a 236
 
7.4%
r 225
 
7.1%
i 211
 
6.6%
n 203
 
6.4%
l 189
 
6.0%
c 148
 
4.7%
h 125
 
3.9%
Other values (37) 1032
32.5%
Common
ValueCountFrequency (%)
201
75.6%
( 23
 
8.6%
) 23
 
8.6%
- 18
 
6.8%
' 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 285
 
8.3%
o 274
 
8.0%
t 245
 
7.1%
a 236
 
6.9%
r 225
 
6.5%
i 211
 
6.1%
n 203
 
5.9%
201
 
5.8%
l 189
 
5.5%
c 148
 
4.3%
Other values (42) 1222
35.5%

District Code
Real number (ℝ)

UNIQUE 

Distinct217
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3635253.5
Minimum0
Maximum39020000
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:37.263310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile292000
Q11410000
median2620000
Q34910000
95-th percentile8282000
Maximum39020000
Range39020000
Interquartile range (IQR)3500000

Descriptive statistics

Standard deviation4097032.9
Coefficient of variation (CV)1.1270281
Kurtosis40.215666
Mean3635253.5
Median Absolute Deviation (MAD)1630000
Skewness5.2035929
Sum7.8885 × 108
Variance1.6785679 × 1013
MonotonicityNot monotonic
2023-07-02T18:20:37.339143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 1
 
0.5%
8550000 1
 
0.5%
2070000 1
 
0.5%
2110000 1
 
0.5%
2120000 1
 
0.5%
7350000 1
 
0.5%
2170000 1
 
0.5%
2100000 1
 
0.5%
7300000 1
 
0.5%
8530000 1
 
0.5%
Other values (207) 207
95.4%
ValueCountFrequency (%)
0 1
0.5%
10000 1
0.5%
70000 1
0.5%
90000 1
0.5%
100000 1
0.5%
140000 1
0.5%
160000 1
0.5%
170000 1
0.5%
200000 1
0.5%
230000 1
0.5%
ValueCountFrequency (%)
39020000 1
0.5%
35060000 1
0.5%
8850000 1
0.5%
8780000 1
0.5%
8760000 1
0.5%
8730000 1
0.5%
8720000 1
0.5%
8550000 1
0.5%
8530000 1
0.5%
8320000 1
0.5%

All Grades
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.331797
Minimum0
Maximum6058
Zeros22
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:37.420718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q320
95-th percentile106.2
Maximum6058
Range6058
Interquartile range (IQR)18

Descriptive statistics

Standard deviation414.17732
Coefficient of variation (CV)7.7660484
Kurtosis207.25947
Mean53.331797
Median Absolute Deviation (MAD)6
Skewness14.250082
Sum11573
Variance171542.85
MonotonicityNot monotonic
2023-07-02T18:20:37.491514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
10.1%
2 22
 
10.1%
1 18
 
8.3%
4 15
 
6.9%
5 11
 
5.1%
3 11
 
5.1%
8 9
 
4.1%
10 8
 
3.7%
7 6
 
2.8%
6 6
 
2.8%
Other values (50) 89
41.0%
ValueCountFrequency (%)
0 22
10.1%
1 18
8.3%
2 22
10.1%
3 11
5.1%
4 15
6.9%
5 11
5.1%
6 6
 
2.8%
7 6
 
2.8%
8 9
4.1%
9 6
 
2.8%
ValueCountFrequency (%)
6058 1
0.5%
420 2
0.9%
395 1
0.5%
374 1
0.5%
259 1
0.5%
231 1
0.5%
177 1
0.5%
132 1
0.5%
127 1
0.5%
107 1
0.5%

Total Students
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1839.5115
Minimum2
Maximum210254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:37.567101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile44
Q1117
median222
Q3564
95-th percentile4194.8
Maximum210254
Range210252
Interquartile range (IQR)447

Descriptive statistics

Standard deviation14388.88
Coefficient of variation (CV)7.8221203
Kurtosis206.48854
Mean1839.5115
Median Absolute Deviation (MAD)138
Skewness14.214198
Sum399174
Variance2.0703988 × 108
MonotonicityNot monotonic
2023-07-02T18:20:37.637271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216 3
 
1.4%
75 3
 
1.4%
63 3
 
1.4%
66 3
 
1.4%
309 2
 
0.9%
203 2
 
0.9%
401 2
 
0.9%
71 2
 
0.9%
44 2
 
0.9%
319 2
 
0.9%
Other values (180) 193
88.9%
ValueCountFrequency (%)
2 1
0.5%
10 1
0.5%
20 1
0.5%
23 1
0.5%
27 1
0.5%
30 1
0.5%
31 1
0.5%
33 1
0.5%
39 1
0.5%
42 1
0.5%
ValueCountFrequency (%)
210254 1
0.5%
19147 1
0.5%
15302 1
0.5%
11822 1
0.5%
10801 1
0.5%
10499 1
0.5%
5446 1
0.5%
5365 1
0.5%
5286 1
0.5%
4893 1
0.5%

Hispanic-Latino-%
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct190
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1141796
Minimum0
Maximum52.586207
Zeros22
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:37.708938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1049724
median2.4728589
Q35.8139535
95-th percentile18.821378
Maximum52.586207
Range52.586207
Interquartile range (IQR)4.7089811

Descriptive statistics

Standard deviation7.4598356
Coefficient of variation (CV)1.4586573
Kurtosis11.923355
Mean5.1141796
Median Absolute Deviation (MAD)1.7429319
Skewness3.0581902
Sum1109.777
Variance55.649146
MonotonicityNot monotonic
2023-07-02T18:20:37.784023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
10.1%
2.314814815 2
 
0.9%
0.7407407407 2
 
0.9%
1.941747573 2
 
0.9%
4 2
 
0.9%
3.333333333 2
 
0.9%
2.272727273 2
 
0.9%
0.3558718861 1
 
0.5%
9.677419355 1
 
0.5%
39.83050847 1
 
0.5%
Other values (180) 180
82.9%
ValueCountFrequency (%)
0 22
10.1%
0.08312551953 1
 
0.5%
0.2173913043 1
 
0.5%
0.269541779 1
 
0.5%
0.3076923077 1
 
0.5%
0.3236245955 1
 
0.5%
0.347826087 1
 
0.5%
0.3546099291 1
 
0.5%
0.3558718861 1
 
0.5%
0.3614768913 1
 
0.5%
ValueCountFrequency (%)
52.5862069 1
0.5%
39.83050847 1
0.5%
37.70014556 1
0.5%
35.13513514 1
0.5%
28.38427948 1
0.5%
27.11864407 1
0.5%
26.19047619 1
0.5%
23.80952381 1
0.5%
22.72727273 1
0.5%
20.83333333 1
0.5%

Primary
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9953917
Minimum0
Maximum326
Zeros210
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:37.843376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum326
Range326
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.770802
Coefficient of variation (CV)8.6034831
Kurtosis122.68019
Mean2.9953917
Median Absolute Deviation (MAD)0
Skewness10.63056
Sum650
Variance664.13424
MonotonicityNot monotonic
2023-07-02T18:20:37.889458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 210
96.8%
112 1
 
0.5%
12 1
 
0.5%
15 1
 
0.5%
13 1
 
0.5%
161 1
 
0.5%
11 1
 
0.5%
326 1
 
0.5%
ValueCountFrequency (%)
0 210
96.8%
11 1
 
0.5%
12 1
 
0.5%
13 1
 
0.5%
15 1
 
0.5%
112 1
 
0.5%
161 1
 
0.5%
326 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
161 1
 
0.5%
112 1
 
0.5%
15 1
 
0.5%
13 1
 
0.5%
12 1
 
0.5%
11 1
 
0.5%
0 210
96.8%

Secondary
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.700461
Minimum0
Maximum1639
Zeros174
Zeros (%)80.2%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:37.948514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile34.2
Maximum1639
Range1639
Interquartile range (IQR)0

Descriptive statistics

Standard deviation113.63407
Coefficient of variation (CV)8.2941786
Kurtosis196.26937
Mean13.700461
Median Absolute Deviation (MAD)0
Skewness13.731243
Sum2973
Variance12912.702
MonotonicityNot monotonic
2023-07-02T18:20:38.008465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 174
80.2%
4 5
 
2.3%
1 3
 
1.4%
6 3
 
1.4%
2 2
 
0.9%
35 2
 
0.9%
10 2
 
0.9%
28 2
 
0.9%
5 2
 
0.9%
16 2
 
0.9%
Other values (20) 20
 
9.2%
ValueCountFrequency (%)
0 174
80.2%
1 3
 
1.4%
2 2
 
0.9%
3 1
 
0.5%
4 5
 
2.3%
5 2
 
0.9%
6 3
 
1.4%
7 1
 
0.5%
10 2
 
0.9%
11 1
 
0.5%
ValueCountFrequency (%)
1639 1
0.5%
224 1
0.5%
208 1
0.5%
108 1
0.5%
104 1
0.5%
82 1
0.5%
77 1
0.5%
64 1
0.5%
42 1
0.5%
35 2
0.9%

High
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.635945
Minimum0
Maximum4093
Zeros26
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:38.086412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q313
95-th percentile93.6
Maximum4093
Range4093
Interquartile range (IQR)11

Descriptive statistics

Standard deviation280.16888
Coefficient of variation (CV)7.6473771
Kurtosis206.13867
Mean36.635945
Median Absolute Deviation (MAD)4
Skewness14.198227
Sum7950
Variance78494.603
MonotonicityNot monotonic
2023-07-02T18:20:38.156064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 26
 
12.0%
2 24
 
11.1%
1 19
 
8.8%
5 15
 
6.9%
4 14
 
6.5%
3 13
 
6.0%
8 10
 
4.6%
10 8
 
3.7%
11 7
 
3.2%
6 7
 
3.2%
Other values (38) 74
34.1%
ValueCountFrequency (%)
0 26
12.0%
1 19
8.8%
2 24
11.1%
3 13
6.0%
4 14
6.5%
5 15
6.9%
6 7
 
3.2%
7 7
 
3.2%
8 10
 
4.6%
9 6
 
2.8%
ValueCountFrequency (%)
4093 1
0.5%
415 1
0.5%
291 1
0.5%
231 1
0.5%
177 1
0.5%
150 1
0.5%
132 1
0.5%
127 1
0.5%
107 1
0.5%
106 1
0.5%

District Name_y
Text

UNIQUE 

Distinct217
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:38.290980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length61
Median length57
Mean length15.847926
Min length4

Characters and Unicode

Total characters3439
Distinct characters52
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique217 ?
Unique (%)100.0%

Sample

1st rowAbington
2nd rowAcademy Of the Pacific Rim Charter Public (District)
3rd rowActon-Boxborough
4th rowAdvanced Math and Science Academy Charter (District)
5th rowAmesbury
ValueCountFrequency (%)
district 27
 
6.5%
charter 22
 
5.3%
regional 18
 
4.3%
vocational 15
 
3.6%
technical 15
 
3.6%
school 13
 
3.1%
academy 10
 
2.4%
public 6
 
1.4%
science 5
 
1.2%
north 5
 
1.2%
Other values (240) 282
67.5%
2023-07-02T18:20:38.523013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 285
 
8.3%
o 274
 
8.0%
t 245
 
7.1%
a 236
 
6.9%
r 225
 
6.5%
i 211
 
6.1%
n 203
 
5.9%
201
 
5.8%
l 189
 
5.5%
c 148
 
4.3%
Other values (42) 1222
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2734
79.5%
Uppercase Letter 439
 
12.8%
Space Separator 201
 
5.8%
Open Punctuation 23
 
0.7%
Close Punctuation 23
 
0.7%
Dash Punctuation 18
 
0.5%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 285
10.4%
o 274
10.0%
t 245
9.0%
a 236
 
8.6%
r 225
 
8.2%
i 211
 
7.7%
n 203
 
7.4%
l 189
 
6.9%
c 148
 
5.4%
h 125
 
4.6%
Other values (14) 593
21.7%
Uppercase Letter
ValueCountFrequency (%)
S 49
11.2%
C 46
 
10.5%
D 38
 
8.7%
M 34
 
7.7%
R 29
 
6.6%
A 26
 
5.9%
W 25
 
5.7%
B 24
 
5.5%
T 24
 
5.5%
P 22
 
5.0%
Other values (13) 122
27.8%
Space Separator
ValueCountFrequency (%)
201
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3173
92.3%
Common 266
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 285
 
9.0%
o 274
 
8.6%
t 245
 
7.7%
a 236
 
7.4%
r 225
 
7.1%
i 211
 
6.6%
n 203
 
6.4%
l 189
 
6.0%
c 148
 
4.7%
h 125
 
3.9%
Other values (37) 1032
32.5%
Common
ValueCountFrequency (%)
201
75.6%
( 23
 
8.6%
) 23
 
8.6%
- 18
 
6.8%
' 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 285
 
8.3%
o 274
 
8.0%
t 245
 
7.1%
a 236
 
6.9%
r 225
 
6.5%
i 211
 
6.1%
n 203
 
5.9%
201
 
5.8%
l 189
 
5.5%
c 148
 
4.3%
Other values (42) 1222
35.5%

Tests Taken
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.700461
Minimum1
Maximum1812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:38.608473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q37
95-th percentile30
Maximum1812
Range1811
Interquartile range (IQR)5

Descriptive statistics

Standard deviation124.34852
Coefficient of variation (CV)7.4458134
Kurtosis203.83952
Mean16.700461
Median Absolute Deviation (MAD)2
Skewness14.096154
Sum3624
Variance15462.553
MonotonicityNot monotonic
2023-07-02T18:20:38.670683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 50
23.0%
1 43
19.8%
3 29
13.4%
4 20
 
9.2%
5 13
 
6.0%
7 12
 
5.5%
6 7
 
3.2%
10 4
 
1.8%
8 4
 
1.8%
9 4
 
1.8%
Other values (25) 31
14.3%
ValueCountFrequency (%)
1 43
19.8%
2 50
23.0%
3 29
13.4%
4 20
 
9.2%
5 13
 
6.0%
6 7
 
3.2%
7 12
 
5.5%
8 4
 
1.8%
9 4
 
1.8%
10 4
 
1.8%
ValueCountFrequency (%)
1812 1
0.5%
248 1
0.5%
100 1
0.5%
99 1
0.5%
98 1
0.5%
72 1
0.5%
70 1
0.5%
45 1
0.5%
39 1
0.5%
36 1
0.5%

% Score 1-2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.429493
Minimum0
Maximum100
Zeros182
Zeros (%)83.9%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:38.737779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile83.3
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.152809
Coefficient of variation (CV)2.507582
Kurtosis3.9505812
Mean10.429493
Median Absolute Deviation (MAD)0
Skewness2.3416947
Sum2263.2
Variance683.9694
MonotonicityNot monotonic
2023-07-02T18:20:38.802489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 182
83.9%
63 2
 
0.9%
100 2
 
0.9%
75 2
 
0.9%
83.3 2
 
0.9%
91.7 2
 
0.9%
81.8 1
 
0.5%
73.3 1
 
0.5%
60 1
 
0.5%
90 1
 
0.5%
Other values (21) 21
 
9.7%
ValueCountFrequency (%)
0 182
83.9%
5.3 1
 
0.5%
7.7 1
 
0.5%
10 1
 
0.5%
26.9 1
 
0.5%
28.6 1
 
0.5%
33.3 1
 
0.5%
35.7 1
 
0.5%
42.9 1
 
0.5%
45 1
 
0.5%
ValueCountFrequency (%)
100 2
0.9%
94.9 1
0.5%
92 1
0.5%
91.7 2
0.9%
91.3 1
0.5%
90 1
0.5%
89.8 1
0.5%
84.2 1
0.5%
83.3 2
0.9%
81.8 1
0.5%

% Score 3-5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6995392
Minimum0
Maximum94.7
Zeros184
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-07-02T18:20:38.866604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile43.76
Maximum94.7
Range94.7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.894585
Coefficient of variation (CV)2.9642019
Kurtosis12.19878
Mean5.6995392
Median Absolute Deviation (MAD)0
Skewness3.4681535
Sum1236.8
Variance285.42699
MonotonicityNot monotonic
2023-07-02T18:20:38.928612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 184
84.8%
25 2
 
0.9%
8.3 2
 
0.9%
16.7 2
 
0.9%
37 2
 
0.9%
50 1
 
0.5%
18.2 1
 
0.5%
26.7 1
 
0.5%
40 1
 
0.5%
10 1
 
0.5%
Other values (20) 20
 
9.2%
ValueCountFrequency (%)
0 184
84.8%
5.1 1
 
0.5%
8 1
 
0.5%
8.3 2
 
0.9%
8.7 1
 
0.5%
10 1
 
0.5%
10.2 1
 
0.5%
15.8 1
 
0.5%
16.7 2
 
0.9%
18.2 1
 
0.5%
ValueCountFrequency (%)
94.7 1
0.5%
92.3 1
0.5%
90 1
0.5%
73.1 1
0.5%
71.4 1
0.5%
66.7 1
0.5%
64.3 1
0.5%
57.1 1
0.5%
55 1
0.5%
50 1
0.5%

Interactions

2023-07-02T18:20:35.892057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.274454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.905940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.507840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.091229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.824967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.457114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.093285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.662310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.301626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.954638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.345669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.972080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.575079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.156548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.894036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.523327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.157585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.728292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.364247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:36.013425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.408000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.032705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.634362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.216481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.957753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.590137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.217722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.799866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.425312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:36.067866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.467764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.090890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.687742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.272790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.021931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.647168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.272690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.864428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.483871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:36.123183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.528443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.150353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.745385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.329111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.087360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.705042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.327161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.927037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.540761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:36.181065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.598119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.212175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.804896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.389116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.157849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.766321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.382719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.991777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.599005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:36.239214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.661802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.272291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.864896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.449257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.218657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.828902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.440420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.064504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.659516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:36.292726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.721325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.329007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.919172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.646918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.281540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.885772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.493906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.122326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.715356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:36.357497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.787109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.393796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.979829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.709846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.343070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.959525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.554119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.186364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.778226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:36.412104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:30.848298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:31.451778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.037252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:32.766357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:33.401798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.032486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:34.610186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.246539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:35.834896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-07-02T18:20:38.992281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
District CodeAll GradesTotal StudentsHispanic-Latino-%PrimarySecondaryHighTests Taken% Score 1-2% Score 3-5
District Code1.000-0.232-0.3290.046-0.114-0.144-0.204-0.116-0.139-0.190
All Grades-0.2321.0000.5430.6420.2610.4590.9140.4460.4210.459
Total Students-0.3290.5431.000-0.1870.0790.0420.6090.5900.5360.518
Hispanic-Latino-%0.0460.642-0.1871.0000.2420.4840.495-0.030-0.0160.016
Primary-0.1140.2610.0790.2421.0000.3430.1730.1320.1970.222
Secondary-0.1440.4590.0420.4840.3431.0000.1580.1100.0870.097
High-0.2040.9140.6090.4950.1730.1581.0000.4620.4370.480
Tests Taken-0.1160.4460.590-0.0300.1320.1100.4621.0000.6420.619
% Score 1-2-0.1390.4210.536-0.0160.1970.0870.4370.6421.0000.937
% Score 3-5-0.1900.4590.5180.0160.2220.0970.4800.6190.9371.000

Missing values

2023-07-02T18:20:36.493489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-02T18:20:36.598300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

District Name_xDistrict CodeAll GradesTotal StudentsHispanic-Latino-%PrimarySecondaryHighDistrict Name_yTests Taken% Score 1-2% Score 3-5
0Abington100001.0281.00.3558720.00.01.0Abington2.00.00.0
1Academy Of the Pacific Rim Charter Public (District)41200008.0144.05.5555560.00.08.0Academy Of the Pacific Rim Charter Public (District)5.00.00.0
2Acton-Boxborough60000003.0379.00.7915570.00.03.0Acton-Boxborough4.00.00.0
3Advanced Math and Science Academy Charter (District)430000026.074.035.1351350.011.015.0Advanced Math and Science Academy Charter (District)3.00.00.0
4Amesbury7000010.0172.05.8139530.00.010.0Amesbury1.00.00.0
5Amherst-Pelham605000065.0229.028.3842790.00.065.0Amherst-Pelham4.00.00.0
6Andover900009.0470.01.9148940.00.09.0Andover3.00.00.0
7Arlington1000003.0412.00.7281550.00.03.0Arlington4.00.00.0
8Ashland1400002.0530.00.3773580.00.02.0Ashland2.00.00.0
9Atlantis Charter (District)49100008.0207.03.8647340.00.08.0Atlantis Charter (District)1.00.00.0
District Name_xDistrict CodeAll GradesTotal StudentsHispanic-Latino-%PrimarySecondaryHighDistrict Name_yTests Taken% Score 1-2% Score 3-5
207Weston330000020.0133.015.0375940.018.02.0Weston2.00.00.0
208Westport33100002.066.03.0303030.00.02.0Westport1.00.00.0
209Westwood33500004.0155.02.5806450.00.04.0Westwood1.00.00.0
210Weymouth336000012.0788.01.5228430.00.012.0Weymouth4.00.00.0
211Whittier Regional Vocational Technical88500000.0323.00.0000000.00.00.0Whittier Regional Vocational Technical3.00.00.0
212Wilmington34200001.0148.00.6756760.00.01.0Wilmington3.00.00.0
213Winchester34400001.0152.00.6578950.00.01.0Winchester6.00.00.0
214Winthrop346000013.0339.03.8348080.00.013.0Winthrop9.00.00.0
215Worcester3480000127.010499.01.2096390.00.0127.0Worcester100.074.026.0
216State Totals06058.0210254.02.881277326.01639.04093.0State Totals1812.063.037.0